@article{(Open Science Index):https://publications.waset.org/pdf/10006805,
	  title     = {Automatic Staging and Subtype Determination for Non-Small Cell Lung Carcinoma Using PET Image Texture Analysis},
	  author    = {Seyhan Karaçavuş and  Bülent Yılmaz and  Ömer Kayaaltı and  Semra İçer and  Arzu Taşdemir and  Oğuzhan Ayyıldız and  Kübra Eset and  Eser Kaya},
	  country	= {},
	  institution	= {},
	  abstract     = {In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.},
	    journal   = {International Journal of Biomedical and Biological Engineering},
	  volume    = {10},
	  number    = {2},
	  year      = {2016},
	  pages     = {123 - 126},
	  ee        = {https://publications.waset.org/pdf/10006805},
	  url   	= {https://publications.waset.org/vol/110},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 110, 2016},
	}